I mean that if we were solving classification, we would have y that tells
us which class each example belongs to. So if we pass this classification's
ground truth vector y to metric learning's fit, we can form S and D inside
by saying that observations from the same class should be similar.
> Only being able to "transform" to a distance to the training set is a bit
> limiting
Sorry, I don't understand what you mean by this. Can you elaborate?
The metric does not memorize training samples, it finds a (linear unless
kernelized) transformation that makes similar examples cluster together.
Moreover, since the metric is completely determined by a PSD matrix, we can
compute its square root, and use to transform new data without any
supervision.
On Wed, Mar 18, 2015 at 9:36 PM, Andreas Mueller <t3k...@gmail.com> wrote:
> The issue with having anything else than fit(X, y) would break
> cross_val_score, GridSearchCV and Pipeline.
> I agree that more control is good, but having functions that don't work
> well with the rest of the package is not great.
>
> Only being able to "transform" to a distance to the training set is a bit
> limiting, but I don't see a different way to do
> it within the current API.
> Can you explain this statement a bit more " We can go with usual y vector
> consisting of feature labels" ?
>
> Thanks,
> Andy
>
>
> On 03/18/2015 12:55 PM, Artem wrote:
>
> Well, we could go with fit(X, y), but since algorithms use S and D, it'd
> better to give user a way to specify them directly if (s)he wants to.
> Either way, LMNN works with raw labels, so it doesn't require any changes
> to the existing API.
>
> On Wed, Mar 18, 2015 at 7:26 PM, Gael Varoquaux <
> gael.varoqu...@normalesup.org> wrote:
>
>> On Wed, Mar 18, 2015 at 07:21:18PM +0300, Artem wrote:
>> > As to what y should look like, it depends on what we'd like the
>> algorithm to
>> > do. We can go with usual y vector consisting of feature labels.
>> Actually, LMNN
>> > is done this way, the optimization objective depends on the equality of
>> labels
>> > only. For ITML (any many others) we need sets of (S)imilar and
>> (D)issimilar
>> > pairs, which can also be inferred from labels.
>>
>> > This is a bit less generic since we would imply that similarity is
>> transitive,
>> > and that's not true in a general case. For the general case we'd need a
>> way to
>> > feed in actual pairs. This could be done with fit having 2 optional
>> arguments
>> > (similar and dissimilar) defaulted to None, which are inferred from y
>> in case
>> > of absence.
>>
>> For now, I don't think that we want to add new variants of the
>> scikit-learn API.
>>
>> G
>>
>>
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